Last updated: Feb 28, 2022

Word Embeddings

Word Embedding is a clever algorithm for grouping words with similar meanings or contexts into a vector or group. This is very useful for grouping similar words and further analysing and processing them. Word Embedding can be accomplished using a variety of models, including Word2Vec, GloVe, and the BERT model.

Word Embeddings in NLP EASY

In this article, we will learn how we can use text data as input in machine learning algorithms, what Word Embeddings are, and their use.

Author Prakriti

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Embedding Layers in Keras

The following article will introduce you to word embedding in keras and why it plays an essential role in NLP.

Author soham Medewar

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Word2vec Model

Word2Vec is a popular model for vectorizing and embedding words. It is abbreviated from Words to Vectors, as the name suggests. Word2Vec also includes more advanced algorithms such as Continuous bag of words and Skip Gram. Learn about its implementation and model architectures in this section.

Vectorial Representation of Words EASY

This article will cover the theoretical knowledge about the vectorial representation of words.

Author Siddhant_007

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CBOW VS Skip-Gram EASY

This article will learn about word embedding, word2vec, and specific significant differences between the two techniques of word2vec.

Author Mayank Goyal

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Word Embedding with Gensim

This blog will focus on implementing word2vec using an essential library in NLP called Gensim.

Author Rajkeshav

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OddOneOut Model using Word Embedding EASY

This article explains how to build a model that gives the odd one out of the given words using Word2Vec and Gensim, using the required code.

Author Adya_Tiwari

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Glove Embeddings

GloVe is an abbreviation for Global Words for Word Representation. It is yet another word vectorisation and embedding model. It is an unsupervised algorithm developed by Stanford University researchers. Learn about its implementation and integration with different Deep Learning techniques, such as Transfer Learning.

GloVe Embedding in NLP EASY

In this article, we will study glove embedding, the reason behind using it, and have a detailed explanation about it with the help of an example.

Author Mayank Goyal

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GloVe Implementation EASY

This article explains the Implementation of the GloVe algorithm and how to use it on any dataset. The learning calculation for producing embeddings has been explained creatively.

Author Adya_Tiwari

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Transfer learning with GloVe EASY

In this blog, we will learn about the theoretical and implementation of transfer learning with GloVe.

Author Siddhant_007

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BERT

BERT is an abbreviation for Bidirectional Encoder Representations from Transformers, which is a transformer-based vectorising and embedding model. It was created by Google researchers. Because of the use of transformers and an attention mechanism, it has a remarkably high accuracy.

Introduction to BIRT

In this blog, we will study a famous Natural language processing tool called BIRT and its applications. Let's begin.

Author Rajkeshav

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Sentiment Analysis using BERT EASY

In this blog, we will learn about the theoretical and implementation of Sentiment Analysis using BERT

Author Siddhant_007

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Sentence Prediction with BERT EASY

In this blog, we will learn about the theoretical and implementation of Sentence Prediction with BERT

Author Siddhant_007

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A-Light BERT (ALBERT) EASY

In this blog, we’ll study what ALBERT is & how it is more efficient and better than BERT.

Author Chaturvedi

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